Object sound detection

ABSTRACT

A vehicle system includes a processor and a memory. The memory stores instructions executable by the processor to identify an area of interest from a plurality of areas on a map, to determine that a detected sound is received in a vehicle audio sensor upon determining that a source of the sound is within the area of interest and not another area in the plurality of areas, and to operate the vehicle based at least in part on the detected sound.

BACKGROUND

One or more computers in an autonomous vehicle (or self-driving car) canbe programmed to navigate and operate the vehicle based on vehiclesensor data. The vehicle computers may rely on data from objectdetection sensors to detect objects. However, problems may arise when anobject cannot be reliably identified, for example when an object isoutside a sensor field of view, there is uncertainty about an objectidentified in the sensor data, e.g., due to noise, imprecise data, ablurry image, etc.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram showing a vehicle and a sound source outside thevehicle.

FIG. 2 illustrates the vehicle of FIG. 1 and a plurality of soundsources located inside and outside an area of interest determined withrespect to the vehicle.

FIGS. 3A-3B are a flowchart of an example process for analyzing receivedaudio data and operating the vehicle.

DETAILED DESCRIPTION Introduction

A vehicle system comprises a processor and a memory. The memory storesinstructions executable by the processor to identify an area of interestfrom a plurality of areas on a map, to determine that a detected soundis received in a vehicle audio sensor upon determining that a source ofthe sound is within the area of interest and not another area in theplurality of areas, and to operate the vehicle based at least in part onthe detected sound.

The instructions may further include instructions to ignore the detectedsound upon determining that the source of the detected sound is withoutthe area of interest.

The instructions may further include instructions to identify the areaof interest based in part on a planned route of the vehicle.

The instructions may further include instructions to identify the areaof interest based on at least one of a physical barrier, a roadboundary, and a distance from a current location of the vehicle.

The instructions further include instructions to determine a parameterof the source including at least one of a type, a trajectory, and alocation of the source based on at least an amplitude and a frequency ofthe detected sound, and to determine that the source is included in anobject in the area of interest based on the determined parameter and mapdata.

The instructions may further include instructions to adjust a plannedpath of the vehicle based on a planned route of the vehicle and thedetermined trajectory of the source.

The instructions may further include instructions to determine alocation of the source and to determine that the source is within theidentified area of interest based on the determined location of thesource.

The instructions may further include instructions to determine based ona classification of the source and map data that the classification ofthe source is invalid.

The instructions may further include instructions to determine, based onthe classification of the source, an expected infrastructure componentfor the determined location of the source, and to determine that theclassification of the source is invalid upon determining, based on themap data, that the expected infrastructure component is non-existent atthe location of the source.

The classification may be one of a train, a truck, a bicycle, amotorbike, a passenger vehicle, and an emergency vehicle, and theinfrastructure component is one of a train track and a road.

The instructions may further include instructions to adjust a plannedpath of the vehicle based on the planned route of the vehicle, thedetermined trajectory of the source, and the classification of thesource only upon determining that the source is valid.

Further disclosed herein is a method comprising identifying an area ofinterest from a plurality of areas on a map, determining that a detectedsound is received in a vehicle audio sensor upon determining that asource of the detected sound is within the area of interest and notanother area in the plurality of areas, and operating the vehicle basedat least in part on the detected sound.

The method may further include ignoring the detected sound upondetermining that the source of the detected sound is without the area ofinterest.

The method may further include identifying the area of interest based inpart on a planned route of the vehicle, a physical barrier, a roadboundary, and a distance from a current location of the vehicle.

The method may further include determining a parameter of the sourceincluding at least one of a type, a trajectory, and a location of thesource based on at least an amplitude and a frequency of the detectedsound, and determining that the source is included in an object in thearea of interest based on the determined parameter and map data.

The method may further include adjusting a planned path of the vehiclebased on a planned route of the vehicle and a determined trajectory ofthe source.

The method may further include determining based on a classification ofthe source and map data that the classification of the detected sourceis invalid.

The method may further include determining, based on the classificationof the source, an expected infrastructure component for the location ofthe source, and determining that the classification of the source isinvalid upon determining, based on the map data, that the expectedinfrastructure component is non-existent at the location of the source.

The classification may be one of a train, a truck, a bicycle, apassenger vehicle, and an emergency vehicle, and the infrastructurecomponent is one of a train track and a road.

The method may further include adjusting a planned path of the vehiclebased on a planned route of the vehicle, a determined trajectory of thesource, and the classification of the source only upon determining thatthe detected source is valid.

Further disclosed is a computing device programmed to execute the any ofthe above method steps.

Yet further disclosed is a computer program product, comprising acomputer readable medium storing instructions executable by a computerprocessor, to execute any of the above method steps.

System Elements

One or more computers in an autonomous or semi-autonomous vehicle can beprogrammed to navigate and operate the vehicle based on vehicle sensordata. However, vehicle object detection sensor(s) may fail to detect anobject or provide inconclusive sensor data. In one example, a vehiclecomputer can be programmed to identify an area of interest from aplurality of areas on a map, determine that a detected sound is receivedin a vehicle audio sensor upon determining that a source of the sound iswithin the area of interest and not another area in the plurality ofareas; and operate the vehicle based at least in part on the detectedsound. Thus, by operating the vehicle only based on sources within thearea of interest, the computer 110 may perform less computation and/orconsume fewer processing cycles compared to operating the vehicle basedon sound sources detected anywhere within a detection range of thevehicle.

FIG. 1 illustrates an example host or ego vehicle 100 including acomputer 110, actuator(s) 120, one or more sensors 130A, 130B, and ahuman machine interface (HMI 140). A reference point such as ageometrical center point 150 can be specified for a vehicle 100, e.g., apoint at which respective longitudinal and lateral centerlines of thevehicle 100 intersect. A vehicle 100 may be powered in variety of knownways, e.g., with an electric motor and/or internal combustion engine.

The computer 110 includes a processor and a memory such as are known.The memory includes one or more forms of computer-readable media, andstores instructions executable by the computer 110 for performingvarious operations, including as disclosed herein.

The computer 110 may operate the vehicle 100 in an autonomous orsemi-autonomous mode. For purposes of this disclosure, an autonomousmode is defined as one in which each of vehicle 100 propulsion, braking,and steering are controlled by the computer 110; in a semi-autonomousmode the computer 110 controls one or two of vehicle 100 propulsion,braking, and steering.

The computer 110 may include programming to operate one or more ofvehicle brakes, propulsion (e.g., control of acceleration in the vehicleby controlling one or more of an internal combustion engine, electricmotor, hybrid engine, etc.), steering, climate control, interior and/orexterior lights, etc., as well as to determine whether and when thecomputer 110, as opposed to a human operator, is to control suchoperations.

The computer 110 may include or be communicatively coupled to, e.g., viaa vehicle communications bus as described further below, more than oneprocessor, e.g., controllers or the like included in the vehicle formonitoring and/or controlling various vehicle controllers, e.g., apowertrain controller, a brake controller, a steering controller, etc.The computer 110 is generally arranged for communications on a vehiclecommunication network such as a bus in the vehicle such as a controllerarea network (CAN) or the like.

Via the vehicle network, the computer 110 may transmit messages tovarious devices in the vehicle and/or receive messages from the variousdevices, e.g., the sensors 130A, 130B, actuators 120, etc. Alternativelyor additionally, in cases where the computer 110 actually comprisesmultiple devices, the vehicle communication network may be used forcommunications between devices represented as the computer 110 in thisdisclosure. Further, as mentioned below, various controllers and/orsensors may provide data to the computer 110 via the vehiclecommunication network.

The actuators 120 may be implemented via circuits, chips, or otherelectronic components that can actuate various vehicle subsystems inaccordance with appropriate control signals as is known. The actuators120 may be used to control braking, acceleration, and steering of thevehicle 100. As an example, the vehicle 100 computer 110 may outputcontrol instructions to control the actuators 120.

Vehicle 100 sensors 130A, 130B may provide data encompassing at leastsome of an exterior of the vehicle 130, e.g., a GPS (Global PositioningSystem) sensor, audio sensor, camera, radar, and/or lidar (light imagingdetection and ranging). For example, the vehicle 100 computer 110 may beprogrammed to determine vehicle 100 location coordinates based datareceived from a vehicle 100 GPS sensor 130A, e.g., with respect to X, Yaxes of a Cartesian coordinate system. An origin of the Cartesiancoordinate system may be at an intersection of X, Y axes of theCartesian coordinate system.

The vehicle 100 may include object detection sensors 130A, 130B thatprovide data to determine attributes (i.e., physical descriptions) of anobject such as location, type, dimensions, trajectory, etc., of objectswithin respective fields of view of the sensors 130A, 130B. Using datafusion techniques, the computer 110 may be programmed to determineobject data based on data received from multiple object detectionsensors 130A, 130B, e.g., a camera and a radar sensor 130A, 130B withoverlapping fields of view.

Additionally or alternatively, sensors 130A, 130B can include audiosensors for detecting sound source(s) 160. The audio sensors 130A, 130Bmay be microphone(s) that are mounted to a vehicle 100 body, e.g., on orunderneath an exterior surface of the vehicle 100. The audio sensors130A, 130B may be mounted in different locations, e.g., front, rear,side, etc., of the vehicle 100. In one example, the audio sensors 130A,130B may include an array of microphones included in a housing. Thesound sensors 130A, 130B may be directional microphones. A directionalmicrophone such as cardioid microphone is designed to receive sound froma particular direction, e.g., a right, left, front or rear side of thevehicle 100. The computer 110 may be programmed to receive audio datafrom the audio sensors 130A, 130B. The sound may come from a car, truck,emergency service vehicle (ambulance, police car, fire truck), bicycle,pedestrian, train, bus, etc. In other words, the sound source 160 may beincluded in a car, truck, etc. For example, sound from a vehicle maycome from rotational parts, vibrations in the engine, friction betweentires and road surface, vibrations of an electric motor, wind effect,gears, fans. In another example, sound may come from human pedestrian,bicycles rider, etc. In yet another example, sound may be generated by adevice such as an ambulance siren, a train horn, a vehicle horn, etc.

The computer 110 may be programmed to detect the sound source 160 basedon audio data received from the audio sensors 130A, 130B, and toidentify location and/or trajectory T of the sound source 160 relativeto the vehicle 100 using signal processing techniques. The computer 110may be programmed to determine the sound source 160 location using atriangulation technique and/or other signal processing techniques suchas time difference of arrival, particle velocity probe, etc. Forexample, the computer 110 may be programmed to estimate distances d₁, d₂from a sound source 160 to the vehicle 100 sensors 130A, 130B based onamplitude of sound received at each of the sensors 130A, 130B. Thecomputer 110 may store a location and/or an orientation of audio sensors130A, 130B relative to the vehicle 100 reference point 150, e.g.,three-dimensional coordinates of sensor(s) 130A, 130B relative to thereference point 150. Additionally or alternatively, the computer 110 maystore coordinates of a vector originated from the location of the sensor130A, 130B that defines, e.g., a direction of a microphone polarpattern. Thus, the computer 110 may be programmed to estimate location(longitudinal and lateral coordinate) of the sound source 160 relativeto the vehicle 100 reference points 150. Further, the computer 110 maybe programmed to determine the location of the vehicle 100 referencepoint 150 relative to a map reference point such as a GPS referencepoint. Thus, the computer 110 may be further programmed to estimate thelocation coordinates of the sound source 160 with respect to, e.g., aGPS reference point, based on (i) the determined location of the soundsource 160 relative to the vehicle 100 reference point 150 and (ii) GPSlocation coordinates of the vehicle 100 reference point 150, e.g.,determined based on GPS sensor 130A data.

As discussed above, a sound source 160 may be a moving object such as avehicle, etc., having a trajectory T. A trajectory T for an object isdefined by sets of location coordinates for the object at respectivetimes, e.g., locations determined at periodic times. The computer 110may be programmed to estimate a trajectory T of the sound source 160,e.g., by iteratively determining the location of the sound source 160,e.g., every 100 milliseconds (ms), based on the received audio data anddetermining the trajectory T based on changes of the sound source 160location.

An acoustic signature, in the present context, is a combination ofmultiple sound features, i.e., amplitude and frequency, of a specifictype (or class) of sound source(s) 160. A class of sound sources 160 canbe defined where different sources 160, e.g., respective ambulancesirens, are expected to have a defined acoustic signature. For example,a frequency component may be specified by a Fourier transform of a soundwave. Classes of sound sources 160 may include emergency vehicle(ambulance, police car, fire truck), passenger vehicle, commercialtruck, motorbike, bicycle, pedestrian, etc. In the present context, aclassification of sound sources 160 is defined based on an acousticsignature of the respective sound source 160. The computer 110 memorymay store a set of sound features, i.e., data including an amplitude anda frequency, corresponding to a class of sound source 160. The computer110 may be programmed to classify a sound source 160 based on receivedaudio data and stored sound features of a class of source 160.

The computer 110 may be programmed to determine that a sound source 160is detected upon determining that an amplitude of a received soundexceeds a threshold that is numerically calculated based on the receivedaudio data, e.g., 60 dB (decibel). The threshold may be based on anamplitude of the signal at a source 160, a distance from the source 160to the vehicle 100, environmental factors such as weather condition,etc. In one example, the computer 110 may be programmed to collect audiodata of a variety of sources 160 such as motorcycles, emergencyvehicles, etc., in different conditions and to tune (or adjust) thethreshold. The computer 110 may be programmed to extract the soundfeatures of a sound source 160 using signal processing techniques, e.g.,determining an amplitude, frequency component(s) of sound waves includedin the sound. The computer 110 may be programmed to classify the soundsource 160 based on the extracted feature(s) using sound recognitiontechniques.

FIG. 2 illustrates the vehicle 100 in an area 200. A geographical area200, in the present context, means a two-dimensional area on the map,i.e., on the surface of the earth. Boundaries or edges of an area 200may be defined by global positioning system (GPS) coordinates, e.g., asvertices of a triangular or rectangular area, a center of a circulararea, etc. An area 200 may have any dimensions and/or shape, e.g.,rectangular, oval, circular, non-geometrical shape, etc. The area 200may include one or more roads 220A, 220B, building(s) 230, wall(s) 240,etc. The computer 110 may detect sound sources 160A, 60B, 160C withinthe area 200 based on audio data received from the vehicle 100 audiosensors 130A, 130B. An area 200 may be divided into a plurality of areas200, e.g., multiple portions.

In the present context, the area 200 may be a detection range of thevehicle 100 audio sensor(s) 130A, 130B. In other words, the computer 110may be programmed to receive audio data from the audio sensors 130A,130B of the vehicle 100 and to detect sound from the area 200 (ordetection area 200) based on the received audio data. Thus, an area 200is defined by a detection range of the vehicle 100 audio sensors 130A,130B from (e.g., as a radius from a point 150 around) a vehicle 100, aslimited by physical features of vehicle 100 surroundings, e.g., abuilding 230 height, wall(s) 240, surface features that may affect adetection range of the vehicle 100 audio sensors 130A, 130B. Forexample, a tall building 230 or a wall 240 along a road 220A may limit adetection range of audio sensors 130A, 130B.

The computer 110 may be programmed to extract features of sound receivedfrom each of the sound sources 160A, 160B, 160C, and to classify thesound sources 160A, 160B, 160C based on the extracted features. In oneexample, e.g., to reduce computational tasks of classifying each of thesound sources 160A, 160B, 160C within a detection area 200 of thecomputer 110, the computer 110 can be programmed to identify an area ofinterest 210 from a plurality of areas 200 on a map, determine that adetected sound is received in a vehicle 100 audio sensor 130A, 130B upondetermining that a source 160 of the sound (or sound source 160) iswithin the area of interest 210 and not another area in the plurality ofareas 200, and to operate the vehicle based at least in part on thedetected sound.

In the present context, an area of interest 210 is an area in which adetected object such as a car, truck, pedestrian, train, etc., mayresult in the computer 110 adjusting a vehicle 100 operation, e.g.,steering, braking, and/or acceleration, i.e., .a portion (i.e., some orall) of the detection area 200 in which a sound source 160A, 160B, 160Cmay direct the computer 110 to adjust a vehicle 100 path P, e.g., toprevent a collision, to give way to an emergency vehicle, etc. In otherwords, the area of interest 210 is an area that a vehicle 100 path Pcould cover. Table 1 shows an example set of rules for identifying anarea of interest 210. For example, the computer 110 may be programmed toidentify the area of interest 210 such that the rules of Table 1 aresatisfied. A path P is a straight or curved line on the ground surfacealong which the vehicle 100 traverses. For example, a path P may berepresented by a polynomial of third degree (sometimes referred to as a“path polynomial”) such as Y=aX+bX²+cX³. Y and X represent longitudinaland lateral coordinates, e.g., with respect to the reference point 150.Parameters a, b, and c of such a polynomial may determine a pathcurvature, on which the vehicle 100 travels. A path P may be defined bya vehicle computer 110 based in part on a planned vehicle 100 route, aswell as based on detecting objects, available travel surfaces, etc.Typically a path P is a line on the ground surface starting from acurrent location of the vehicle 100 extending with a maximumpredetermined length, e.g., 100 meters, from the vehicle 100 location.The computer 110 may be programmed to actuate vehicle 100 actuators 120such that the vehicle 100 traverses the path P.

TABLE 1 Rule Description Define according to An area that is within adistance threshold, e.g., 100 meters, of the distance from vehiclevehicle location. Define according to An area that is within apredetermined distance, e.g., 20 meters, from distance from vehicle apath of the vehicle, i.e., a distance from a nearest point of the pathpath to a respective point of the area of interest will be less than thethreshold. Drivable surface An area that is defined as “drivable” suchas road, bicycle and/or pedestrian surface, etc. Thus, areas covered bybuildings, landscaping, etc., will be excluded. Accessible surface Anarea from which there is an available route within the detection area tothe current vehicle location. For example, a wall, building, etc., maymake an area inaccessible, e.g., road 220B is inaccessible from currentlocation of vehicle because of the wall 240.

The computer 110 may be programmed to identify the area of interest 210based on a physical barrier, e.g., a wall 240, a road boundary (orshoulder 260), a building 230, etc. For example, the computer 110 may beprogrammed to determine the area of interest 210 by excluding portionsof the detection area 200 physically separated (or inaccessible) fromvehicle 100 path P. As an example shown in FIG. 2, a highway wall 240may separate the roads 220A, 220B. In the present context, a point isinaccessible from a vehicle 100 path P when no direct line without anyphysical barrier can be drawn from the respective inaccessible point toa point on the path P. For example, the sound source 160B on the road220B is outside the area of interest 210 because the road 220B isinaccessible with respect to the vehicle 100 path P.

Additionally or alternatively, the computer 110 may be programmed toidentify the area of interest 210 based on map data by determining oneor more surfaces within the detection area 200 that are drivable. Mapdata may include location coordinates (e.g., GPS coordinates) ofinfrastructure elements such as roads 220A, 220B, building(s) 230,bicycle and/or pedestrian path(s) 250, vegetation, etc. In the presentcontext, a drivable surface is a surface on which the vehicle 100 canoperate without risk of damage or becoming stuck, and typically includesat least one of a road surface 220A, 20B, a bicycle and/or pedestrianpath 250, a parking space, a driveway, etc. In one example, the computer110 may be programmed to identify drivable surfaces within the area 200and then to identify the area of interest 210 by selecting accessibleportions of the drivable surfaces. For example, as shown in FIG. 2, aroad 220B is a drivable surface but is inaccessible for the vehicle 100because of the wall 240. Therefore, the computer 110 could executeprogramming to exclude the drivable surface of the road 220B from thearea of interest 210.

The computer 110 may be programmed to identify the physical barriersbased on vehicle 100 location data and map data. In one example, thecomputer 110 may be programmed to determine the vehicle 100 locationcoordinates with respect to X, Y axes, e.g., based on data received froma vehicle 100 GPS sensor 130A, and to identify the physical barrierssuch as wall 240, road shoulder 260, etc., within the detection area 200based on map data and the vehicle 100 location.

Additionally or alternatively, the computer 110 may be programmed toidentify the area of interest 210 based in part on a planned route P ofthe vehicle. In one example, the computer 110 may be programmed toidentify the area of interest 210 based on a distance d₃ from a currentlocation of the vehicle 100, e.g., a distance d₃ behind and/or in frontof the vehicle 100 along a path P of the vehicle 100.

As discussed above, the computer 110 may be programmed to determine aparameter of, e.g., the source 160A, including at least one of a type(or classification), a trajectory T, and a location of the source 160Abased on extracted feature(s) of the sound, e.g., an amplitude and/or afrequency of the sound. The computer 110 may be programmed to determinethat the source 160A is included in an object in the area of interest210 based on the determined parameter, e.g., location, and map data.

For example, the computer 110 may be programmed to determine that thesource 160B is outside the area of interest 210 based on identified areaof interest 210 and the detected location and/or trajectory T of thesource 160B. The computer 110 may be programmed to ignore the receivedsound of the source 160B upon determining that the sound source 160B isoutside the area of interest 210. In one example, the computer 110 maybe programmed to classify a sound source 160A, 160C only upondetermining that the sound source 160A, 160C is/are within the area ofinterest 210. This may reduce a computational task of the computer 110compared to classifying each sound source 160A, 160B, 160C within thedetection area 200.

In one example, the computer 110 may misclassify a sound source 160Cthat is determined to be located within the area of interest 210. Forexample, the computer 110 may classify the sound source 160C as a train.The computer 110 may be programmed to determine based on aclassification of the source 160C and the map data that the determinedclassification of the detected source 160C is invalid. For example, thecomputer 110 may be programmed to determine, based on the classificationof the source 160C, an expected infrastructure component for thedetermined location of the source 160C, and to determine that thedetected source 160C is invalid upon determining, based on the map data,that the expected infrastructure component is non-existent at thelocation of the source 160C.

An expected infrastructure component is a physical feature associatedwith a class of sound source 160, e.g., a train track, a road 220A,220B, etc. For example, the computer 110 may be programmed to determinebased on map data that no train track exists at a location of the soundsource 160C and to determine that the classification of the sound source160C with a “train” classification is invalid because the expected traintrack for a train is non-existent at the determined location of thesound source 160C. Thus, the computer 110 may be programmed to concludethat the train classification of the sound source 160C is invalid. Asdiscussed below, the computer 110 may be programmed to operate thevehicle 100 only upon determining that a classified sound source 160A isvalid. As another example, the computer 110 may determine a sound source(not shown) with a vehicle classification at a location of a pedestrianpath 250 is invalid upon determining based on map data that no road220A, 20B exists at the determined location of the sound source.

The computer 110 may be programmed to adjust a planned path P of thevehicle 100 based on the planned route of the vehicle 100, thedetermined trajectory T of the source 160A, and the classification ofthe source 160A only upon determining that the classification of thedetected source 160A is valid. The computer 110 may be programmed toactuate a vehicle 100 actuator 120, e.g., braking, steering, and/orpropulsion, to adjust a vehicle 100 path, to stop, etc., based on thereceived sensor 130A, 130B data. Table 2 shows a set of exemplary rulesfor operating the vehicle 100 based at least in part on a valid soundsource 160A.

TABLE 2 Rule Description Move to shoulder and Actuate vehicle actuatorsto move the vehicle to a road shoulder and stop stop the vehicle upondetecting an emergency vehicle (e.g., police, fire truck, ambulance)with activated siren within the area of interest Adjust path to preventDetermine vehicle path based at least in part based on the detectedcollision non-emergency sound source, e.g., truck, car, bicycle,motorbike, pedestrian, etc. For example, the vehicle path within thelane may be adjusted, e.g., by moving near a right edge of the lane, toallow a motorbike moving between two lanes to pass by.

FIGS. 3A-3B are a flowchart of an example process 300 for analyzingreceived audio data and operating the vehicle 100. For example, avehicle 100 computer 110 may be programmed to execute blocks of theprocess 300.

With reference to FIG. 3A, the process 300 begins in a block 305, inwhich the computer 110 receives map data, e.g., from a remote computer.Additionally or alternatively, the map data may be stored in a computermemory in the vehicle 100.

Next, in a block 310, the computer 110 receives vehicle 100 locationdata. For example, the computer 110 receives GPS location coordinatesfrom a vehicle 100 GPS sensor 130A, 130B. Additionally or alternatively,the computer 110 may be programmed to determine the vehicle 100 locationusing a localization technique, e.g., based on lidar (Light detectionand ranging) data, etc.

Next, in a block 315, the computer 110 receives audio data. The computer110 may receive audio data from one or more audio sensors 130A, 130Blocated in different location(s) of the vehicle 100.

Next, in a block 320, the computer 110 receives vehicle 100 path P data.The computer 110 may be programmed to determine the path P based on,e.g., a destination location data received from a vehicle 100 HMI 140,data stored in a computer 110 memory, etc.

Next, in a block 330, the computer 110 determines an area of interest210. In one example, the computer 110 may be programmed to identify thearea of interest 210 based on a set of rules, e.g., as shown in ofTable 1. The computer 110 may be programmed to identify an area ofinterest 210 for example as described above based on map data, physicalbarriers, a path P of the vehicle 100, location of the vehicle 100, etc.

With reference to FIG. 3B, next in a decision block 335, the computer110 determines whether a sound source 160A, 160B, 160C (FIG. 2) iswithin the area of interest 210. The computer 110 may be programmed todetermine whether the sound source 160A, 160B, 160C is within the areaof interest 210 based on the location of the sound source 160A, 60B,160C. If the computer 110 determines that the sound source 160A, 160B,160C is within the area of interest 210, then the process 300 proceedsto a block 340; otherwise the process 300 proceeds to a block 355.

In the block 340, the computer 110 extracts the sound features of thereceived sound. For example, the computer 110 may be programmed todetermine frequency component(s) of the received sound.

Next, in a block 345, the computer 110 classifies the sound source 160A,160C based on the extracted sound features. For example, the computer110 may be programmed to classify sound sources 160A, 160C to car,truck, emergency vehicle, train, pedestrian, bicycle, etc.

Next, in a decision block 350, the computer 110 determines whether thedetected sound source 160A, 160C is valid or invalid, i.e., correctlyclassified or incorrectly classified. For example, the computer 110 maybe programmed to determine that the sound source 160C classified as atrain is invalid upon determining, based on the map data, that theexpected infrastructure component is non-existent at the location of thesource 160C. If the computer 110 determines that the sound source 160Cis invalid, then the process 300 proceeds to a block 360; otherwise theprocess 300 proceeds to the block 355.

In the block 355, which can be reached from either of decision blocks335, 350, the computer 110 ignores the detected sound source andproceeds to a block 365. For example, when the block 355 is reached fromthe decision block 335, the computer 110 ignores the sound source 160Boutside the area of interest 210. When the block 355 is reached from thedecision block 350, the computer 110 ignores the invalid sound source160C.

In the block 360, the computer 110 adjusts the vehicle 100 path P basedon the parameters such as classification, trajectory T, location, etc.,of the detected sound source 160. The computer 110 may be programmed,e.g., based on the exemplary rules of Table 2, to adjust the vehicle 100path P to a point on a shoulder 260 of the road 220A upon detecting asound source 160A with an emergency vehicle classification. In anotherexample, the computer 110 may be programmed to adjust the vehicle 100path P to prevent a collision with a sound source 160A with aclassification as pedestrian, vehicle, etc., e.g., using collisionavoidance techniques including emergency braking, collision avoidancemaneuver (e.g., by changing lane).

Next, in a block 365 that can be reached from either of blocks 355, 360,the computer 110 operates the vehicle 100 by actuating vehicle 100actuators 120 such as braking, steering, and/or propulsion actuators120. The computer 110 may be programmed to actuate the vehicle 100actuators 120 to move the vehicle 100 along the path P, e.g., accordingto existing drive-by-wire techniques used to operate vehiclesautonomously or semi-autonomously. When the block 365 is reached fromthe block 360, the computer 110 may actuate the vehicle 100 actuators120 based on the adjusted path P, e.g., the adjusted path P to the roadshoulder 260.

Following the block 365, the process 300 ends, or alternatively, returnsto the block 305.

Computing devices as discussed herein generally each includeinstructions executable by one or more computing devices such as thoseidentified above, and for carrying out blocks or steps of processesdescribed above. Computer-executable instructions may be compiled orinterpreted from computer programs created using a variety ofprogramming languages and/or technologies, including, withoutlimitation, and either alone or in combination, Java™, C, C++, VisualBasic, Java Script, Perl, HTML, etc. In general, a processor (e.g., amicroprocessor) receives instructions, e.g., from a memory, acomputer-readable medium, etc., and executes these instructions, therebyperforming one or more processes, including one or more of the processesdescribed herein. Such instructions and other data may be stored andtransmitted using a variety of computer-readable media. A file in thecomputing device is generally a collection of data stored on a computerreadable medium, such as a storage medium, a random-access memory, etc.

A computer-readable medium includes any medium that participates inproviding data (e.g., instructions), which may be read by a computer.Such a medium may take many forms, including, but not limited to,non-volatile media, volatile media, etc. Non-volatile media include, forexample, optical or magnetic disks and other persistent memory. Volatilemedia include dynamic random-access memory (DRAM), which typicallyconstitutes a main memory. Common forms of computer-readable mediainclude, for example, a floppy disk, a flexible disk, hard disk,magnetic tape, any other magnetic medium, a CD-ROM, DVD, any otheroptical medium, punch cards, paper tape, any other physical medium withpatterns of holes, a RAM, a PROM, an EPROM, a FLASH, an EEPROM, anyother memory chip or cartridge, or any other medium from which acomputer can read.

With regard to the media, processes, systems, methods, etc. describedherein, it should be understood that, although the steps of suchprocesses, etc. have been described as occurring according to a certainordered sequence, such processes could be practiced with the describedsteps performed in an order other than the order described herein. Itfurther should be understood that certain steps could be performedsimultaneously, that other steps could be added, or that certain stepsdescribed herein could be omitted. In other words, the descriptions ofsystems and/or processes herein are provided for the purpose ofillustrating certain embodiments, and should in no way be construed soas to limit the disclosed subject matter.

Accordingly, it is to be understood that the present disclosure,including the above description and the accompanying figures and belowclaims, is intended to be illustrative and not restrictive. Manyembodiments and applications other than the examples provided would beapparent to those of skill in the art upon reading the abovedescription. The scope of the invention should be determined, not withreference to the above description, but should instead be determinedwith reference to claims appended hereto and/or included in anon-provisional patent application based hereon, along with the fullscope of equivalents to which such claims are entitled. It isanticipated and intended that future developments will occur in the artsdiscussed herein, and that the disclosed systems and methods will beincorporated into such future embodiments. In sum, it should beunderstood that the disclosed subject matter is capable of modificationand variation.

What is claimed is:
 1. A vehicle system, comprising a processor and amemory, the memory storing instructions executable by the processor to:identify an area of interest from a plurality of areas on a map;determine that a detected sound is received in a vehicle audio sensorupon determining that a source of the sound is within the area ofinterest and not another area in the plurality of areas; and operate thevehicle based at least in part on the detected sound.
 2. The system ofclaim 1, wherein the instructions further include instructions to ignorethe detected sound upon determining that the source of the detectedsound is without the area of interest.
 3. The vehicle system of claim 1,wherein the instructions further include instructions to identify thearea of interest based in part on a planned route of the vehicle.
 4. Thevehicle system of claim 1, wherein the instructions further includeinstructions to identify the area of interest based on at least one of aphysical barrier, a road boundary, and a distance from a currentlocation of the vehicle.
 5. The vehicle system of claim 1, wherein theinstructions further include instructions to: determine a parameter ofthe source including at least one of a type, a trajectory, and alocation of the source based on at least an amplitude and a frequency ofthe detected sound; and determine that the source is included in anobject in the area of interest based on the determined parameter and mapdata.
 6. The vehicle system of claim 5, wherein the instructions furtherinclude instructions to adjust a planned path of the vehicle based on aplanned route of the vehicle and the determined trajectory of thesource.
 7. The vehicle system of claim 1, wherein the instructionsfurther include instructions to determine a location of the source andto determine that the source is within the identified area of interestbased on the determined location of the source.
 8. The vehicle system ofclaim 7, wherein the instructions further include instructions todetermine based on a classification of the source and map data that theclassification of the source is invalid.
 9. The vehicle system of claim8, wherein the instructions further include instructions to: determine,based on the classification of the source, an expected infrastructurecomponent for the determined location of the source; and determine thatthe classification of the source is invalid upon determining, based onthe map data, that the expected infrastructure component is non-existentat the location of the source.
 10. The vehicle system of claim 9,wherein the classification is one of a train, a truck, a bicycle, amotorbike, a passenger vehicle, and an emergency vehicle, and theinfrastructure component is one of a train track and a road.
 11. Thevehicle system of claim 8, wherein the instructions further includeinstructions to adjust a planned path of the vehicle based on theplanned route of the vehicle, the determined trajectory of the source,and the classification of the source only upon determining that thesource is valid.
 12. A method, comprising: identifying an area ofinterest from a plurality of areas on a map; determining that a detectedsound is received in a vehicle audio sensor upon determining that asource of the detected sound is within the area of interest and notanother area in the plurality of areas; and operating the vehicle basedat least in part on the detected sound.
 13. The method of claim 12,further comprising ignoring the detected sound upon determining that thesource of the detected sound is without the area of interest.
 14. Themethod of claim 12, further comprising identifying the area of interestbased in part on a planned route of the vehicle, a physical barrier, aroad boundary, and a distance from a current location of the vehicle.15. The method of claim 12, further comprising: determining a parameterof the source including at least one of a type, a trajectory, and alocation of the source based on at least an amplitude and a frequency ofthe detected sound; and determining that the source is included in anobject in the area of interest based on the determined parameter and mapdata.
 16. The method of claim 12, further comprising adjusting a plannedpath of the vehicle based on a planned route of the vehicle and adetermined trajectory of the source.
 17. The method of claim 12, furthercomprising determining based on a classification of the source and mapdata that the classification of the detected source is invalid.
 18. Themethod of claim 17, further comprising: determining, based on theclassification of the source, an expected infrastructure component forthe location of the source; and determining that the classification ofthe source is invalid upon determining, based on the map data, that theexpected infrastructure component is non-existent at the location of thesource.
 19. The method of claim 18, wherein the classification is one ofa train, a truck, a bicycle, a motorbike, a passenger vehicle, and anemergency vehicle, and the infrastructure component is one of a traintrack and a road.
 20. The method of claim 18, further comprisingadjusting a planned path of the vehicle based on a planned route of thevehicle, a determined trajectory of the source, and the classificationof the source only upon determining that the detected source is valid.